Investigating the beneficial impact of ...
Document type :
Compte-rendu et recension critique d'ouvrage
Title :
Investigating the beneficial impact of segmentation-based modelling for credit scoring
Author(s) :
Idbenjra, Khaoula [Auteur]
Lille économie management - UMR 9221 [LEM]
Coussement, Kristof [Auteur]
Lille économie management - UMR 9221 [LEM]
De Caigny, Arno [Auteur]
Lille économie management - UMR 9221 [LEM]
Lille économie management - UMR 9221 [LEM]
Coussement, Kristof [Auteur]
![refId](/themes/Mirage2//images/idref.png)
Lille économie management - UMR 9221 [LEM]
De Caigny, Arno [Auteur]
Lille économie management - UMR 9221 [LEM]
Journal title :
Decision Support Systems
Pages :
114170
Publisher :
Elsevier
Publication date :
2024-04
ISSN :
0167-9236
HAL domain(s) :
Sciences de l'Homme et Société/Gestion et management
English abstract : [en]
Due to its vital role in financial risk management, credit scoring has been investigated extensively in extant information systems studies. However, most credit scoring studies rely on one-size-fits-all classifiers with ...
Show more >Due to its vital role in financial risk management, credit scoring has been investigated extensively in extant information systems studies. However, most credit scoring studies rely on one-size-fits-all classifiers with logistic regression (LR) as a popular benchmark. Moreover, extant literature largely focuses on predictive performance as an evaluation criterion. To find a better balance between predictive performance and interpretability though, the current study investigates the beneficial impact of segmentation-based modelling by benchmarking the logit leaf model (LLM) which is based on LR and decision trees. By a large experimental setup using a real-life credit scoring data set containing 65,536 active customers, we find that LLM is a viable classifier over its constituent parts, i.e., LR and decision trees, and is very competitive to state-of-the-art credit decision making techniques (neural networks, support vector machines, bagging, boosting and random forests) on three evaluation metrics (AUC, top-decile lift and profit). Furthermore, we show its extraordinary interpretability capacities by proposing a new visualization based on the LLM output. In sum, the excellence of the LLM as a classifier for credit decision making problems stems from its ability to combine strong predictive performance with interpretable insights that in turn can inform managerial decisions.Show less >
Show more >Due to its vital role in financial risk management, credit scoring has been investigated extensively in extant information systems studies. However, most credit scoring studies rely on one-size-fits-all classifiers with logistic regression (LR) as a popular benchmark. Moreover, extant literature largely focuses on predictive performance as an evaluation criterion. To find a better balance between predictive performance and interpretability though, the current study investigates the beneficial impact of segmentation-based modelling by benchmarking the logit leaf model (LLM) which is based on LR and decision trees. By a large experimental setup using a real-life credit scoring data set containing 65,536 active customers, we find that LLM is a viable classifier over its constituent parts, i.e., LR and decision trees, and is very competitive to state-of-the-art credit decision making techniques (neural networks, support vector machines, bagging, boosting and random forests) on three evaluation metrics (AUC, top-decile lift and profit). Furthermore, we show its extraordinary interpretability capacities by proposing a new visualization based on the LLM output. In sum, the excellence of the LLM as a classifier for credit decision making problems stems from its ability to combine strong predictive performance with interpretable insights that in turn can inform managerial decisions.Show less >
Language :
Anglais
Popular science :
Non
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